Load Packages

library(dplyr)
Warning: package ‘dplyr’ was built under R version 4.0.5

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union
library(tidyverse)
Warning: package ‘tidyverse’ was built under R version 4.0.5
Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
-- Attaching packages -------------------------------------- tidyverse 1.3.1 --
v ggplot2 3.3.5     v purrr   0.3.4
v tibble  3.1.5     v stringr 1.4.0
v tidyr   1.1.4     v forcats 0.5.1
v readr   2.0.2     
Warning: package ‘ggplot2’ was built under R version 4.0.5
Warning: package ‘tibble’ was built under R version 4.0.5
Warning: package ‘tidyr’ was built under R version 4.0.5
Warning: package ‘readr’ was built under R version 4.0.5
Warning: package ‘purrr’ was built under R version 4.0.5
Warning: package ‘stringr’ was built under R version 4.0.3
Warning: package ‘forcats’ was built under R version 4.0.5
-- Conflicts ----------------------------------------- tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
library(ggplot2)
library(janitor)
Warning: package ‘janitor’ was built under R version 4.0.5

Attaching package: ‘janitor’

The following objects are masked from ‘package:stats’:

    chisq.test, fisher.test
library(lubridate)
Warning: package ‘lubridate’ was built under R version 4.0.5

Attaching package: ‘lubridate’

The following objects are masked from ‘package:base’:

    date, intersect, setdiff, union
library(tidymodels)
Warning: package ‘tidymodels’ was built under R version 4.0.5
Registered S3 method overwritten by 'tune':
  method                   from   
  required_pkgs.model_spec parsnip
-- Attaching packages ------------------------------------- tidymodels 0.1.4 --
v broom        0.7.9      v rsample      0.1.0 
v dials        0.0.10     v tune         0.1.6 
v infer        1.0.0      v workflows    0.2.3 
v modeldata    0.1.1      v workflowsets 0.1.0 
v parsnip      0.1.7      v yardstick    0.0.8 
v recipes      0.1.17     
Warning: package ‘broom’ was built under R version 4.0.5
Warning: package ‘dials’ was built under R version 4.0.5
Warning: package ‘scales’ was built under R version 4.0.3
Warning: package ‘infer’ was built under R version 4.0.5
Warning: package ‘modeldata’ was built under R version 4.0.5
Warning: package ‘parsnip’ was built under R version 4.0.5
Warning: package ‘recipes’ was built under R version 4.0.5
Warning: package ‘rsample’ was built under R version 4.0.5
Warning: package ‘tune’ was built under R version 4.0.5
Warning: package ‘workflows’ was built under R version 4.0.5
Warning: package ‘workflowsets’ was built under R version 4.0.5
Warning: package ‘yardstick’ was built under R version 4.0.5
-- Conflicts ---------------------------------------- tidymodels_conflicts() --
x scales::discard() masks purrr::discard()
x dplyr::filter()   masks stats::filter()
x recipes::fixed()  masks stringr::fixed()
x dplyr::lag()      masks stats::lag()
x yardstick::spec() masks readr::spec()
x recipes::step()   masks stats::step()
* Use tidymodels_prefer() to resolve common conflicts.
library(httr)
Warning: package ‘httr’ was built under R version 4.0.5
library(jsonlite)
Warning: package ‘jsonlite’ was built under R version 4.0.5

Attaching package: ‘jsonlite’

The following object is masked from ‘package:purrr’:

    flatten
library(sf)
Warning: package ‘sf’ was built under R version 4.0.5
Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1
library(tmap)
Warning: package ‘tmap’ was built under R version 4.0.5
Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio

Load Data

raw_sr_data <- read.csv("SR_data.csv") %>%
  clean_names()

Just ADK

raw_adk_data <- raw_sr_data %>%
  filter(incident_adirondack_park == "true")

All Data Map

raw_sr_map <- raw_sr_data[complete.cases(raw_sr_data), ] %>%
  # select(location_found_latitude,location_found_longitude) %>%
st_as_sf(coords = c("location_found_longitude", "location_found_latitude"), crs = 4326)
tmap_mode("view")
tmap mode set to interactive viewing
tm_shape(raw_sr_map) +
  # tm_borders() +
  # tm_shape(adk_geom_data) +
  tm_dots(size=0.02,col="red", alpha = 0.5) + tm_legend(outside = TRUE) #tm_text("Locations of Incidents in New York State that Required Ranger Assistance")

Take me on a date

timey_adk_data <- na.omit(raw_adk_data) %>%
  mutate(true_start_date = paste(incident_start_date,"",format(strptime(incident_start_time, "%I:%M %p"), "%H:%M:00"))) %>%
  mutate(true_closed_date = paste(incident_closed_date,"",format(strptime(incident_closed_time, "%I:%M %p"), "%H:%M:00"))) %>%

mutate(actual_start = mdy_hms(true_start_date)) %>%
mutate(actual_closed = mdy_hms(true_closed_date)) %>%
  mutate(duration = (actual_closed-actual_start))

Not ADK

raw_not_adk_data <- raw_sr_data %>%
  filter(incident_adirondack_park == "false")

How many single people needed rescue

each_incident_num <- table(raw_adk_data$incident_number) %>%
  as.data.frame() %>%
  filter(Freq == 1 )

Counts

count_gender <-  table(raw_adk_data['subject_gender'])
View(count_gender)
count_ages <- table(raw_adk_data['subject_age'])
View(count_ages)
count_rangers <- table(raw_adk_data['number_of_rangers_involved'])
View(count_rangers)
count_elevation <- table(raw_adk_data['location_found_elevation'])
View(count_elevation)
mean(raw_adk_data$subject_age, na.rm = TRUE)
[1] 37.64492
mean(raw_not_adk_data$subject_age, na.rm = TRUE)
[1] 36.93668

Preliminary Plotting

plot_relevant <- raw_adk_data %>%
  ggplot(aes(subject_age,location_found_elevation)) + geom_point()

MAPS!

ADK Region

Relevant Data

adk_geom_data <- raw_adk_data[complete.cases(raw_adk_data), ] %>%
  # select(location_found_latitude,location_found_longitude) %>%
st_as_sf(coords = c("location_found_longitude", "location_found_latitude"), crs = 4326) 
# adk_geom_data <- left_join(adk_geom_data,raw_adk_data[complete.cases(raw_adk_data), ],by=c("code")) 

tmap_mode("view")
tmap mode set to interactive viewing
tm_shape(adk_geom_data) +
  # tm_borders() +
  # tm_shape(adk_geom_data) +
  tm_dots(size=0.02,col="subject_age", alpha = 0.7, palette = "Spectral")
NA
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